Prof. Dr. Frederick Klauschen
Prof. Dr. Frederick Klauschen
Research Group Lead / Charité
Research Grouplead | BIFOLD
Director | Pathologisches Institut, Ludwig-Maximilian-Universität München
Group Leader
Institute of Pathology
Charité UNIVERSITÄTSMEDIZIN BERLIN
| 2012 | Novartis Pathology-Oncology Award |
| 2011 | Human Frontier Science Program Young Investigator Award |
| 2004 | NIH Postdoctoral Fellowship Award |
Systems biological integration of proteogenomic profiles and histological images through bioinformatics and machine learning with the goal to better understand and predict pathological mechanisms in tumors and finally, to better diagnose and treat cancer.
- German Pathological Society
- International Academy of Pathology
- German Physical Society
Philipp Anders, Marvin Sextro, Katja Lingelbach, Kai Standvoss, Suhas Pandhe, Sandip Ghosh, Cornelius Böhm, Stephan Tietz, Rosemarie Krupar, Lars Tharun, Marie-Lisa Eich, Julika Ribbat-Idel, Evelyn Ramberger, Xizi Liang, Verena Aumiller, Sabine Merkelbach-Bruse, Alexander Quaas, Nikolaj Frost, Georg Schlachtenberger, Matthias Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens-Carsten Rückert, Reinhard Büttner, Christian Grohe, David Horst, Maximilian Alber, Lukas Ruff, Frederick Klauschen, Gabriel Dernbach, Philipp Seegerer, Simon Schallenberg
ADC target profiling in NSCLC: Generalizable AI separates TROP-2 and cMET phenotypes
Philipp Jurmeister, Susanne Flach, Linda Bergmayr, Konstanze Schleich, Edgar Chimal Calderon, Liliana H Mochmann, Yauheniya Zhdanovich, Doreen Klingler, Ada Pusztai, Anna Kübler, Christoph Walz, Christoph Benedikt Westphalen, Alexander Beck, Maximilian Leitheiser, Gerben E Breimer, Johannes A Rijken, Lot Devriese, Philipp Baumeister, Alena Skálová, Simon Schallenberg, Frederick Klauschen, Andreas Mock
Spatially resolved ex vivo drug response profiling in SMARCB1-deficient sinonasal carcinoma
Maaike Galama, Nina Kozar-Gillan, Christina Embacher, Todd Dembo, Cornelius Böhm, Evelyn Ramberger, Julika Ribbat-Idel, Rosemarie Krupar, Verena Aumiller, Miriam Hägele, Kai Standvoss, Gerrit Erdmann, Blanca Pablos, Ari Angelo, Simon Schallenberg, Andrew Norgan, Viktor Matyas, Klaus-Robert Müller, Maximilian Alber, Lukas Ruff, Frederick Klauschen
OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
Deema Sabtan, Marie-Lisa Eich, Florian Loch, Julen Karl Pérez Zuschneid, Markus Möbs, Judith Böhme, Frederick Klauschen, David Horst, Mihnea P Dragomir, Gabriel Dernbach, Simon Schallenberg
Spatial heterogeneity of antibody–drug conjugate targets in pancreatic ductal adenocarcinoma
Maximilian Alber, Timo Milbich, Alexandra Carpen-Amarie, Stephan Tietz, Jonas Dippel, Lukas Muttenthaler, Beatriz Perez Cancer, Alessandro Benetti, Panos Korfiatis, Elias Eulig, Jérôme Lüscher, Jiasen Wu, Sayed Abid Hashimi, Gabriel Dernbach, Simon Schallenberg, Neelay Shah, Moritz Krügener, Aniruddh Jammoria, Jake Matras, Patrick Duffy, Matt Redlon, Philipp Jurmeister, David Horst, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen, Andrew Norgan
Atlas 2 - Foundation models for clinical deployment
A benchmark for trustworthy clinical AI
A new study published in Nature Communications shows that today's pathology foundation models can be influenced by the origin of a tissue sample. Researchers at BIFOLD and Aignostics developed PathoROB, a first-of-its-kind benchmark to measure and reduce this bias, shaping how the next generation of pathology AI is built.
AI Improves Lung Cancer Diagnostics
An interdisciplinary research team from BIFOLD (Berlin Institute for the Foundations of Learning and Data), Technische Universität Berlin, Universitätsklinikum Köln, Charité - Universitätsmedizin Berlin, the AI company Aignostics, and Ludwig Maximilians University Munich (LMU) has developed a novel AI-based method to more accurately predict the survival of lung cancer patients.
AI in medicine: new approach for more efficient diagnostics
Researchers from LMU, BIFOLD, and Charité have developed a new AI tool that uses imaging data to also detect less frequent diseases of the gastrointestinal tract. In contrast to conventional models, the new AI only needs training data from common findings to detect deviations.
AI facilitates breakthrough in cancer diagnostics
So-called sinonasal undifferentiated carcinomas (SNUCs) are extremely difficult to diagnose. An interdisciplinary team of researchers has developed an AI tool that reliably distinguishes tumors on the basis of chemical DNA modifications
An overview of the current state of research in BIFOLD
Since the official announcement of the Berlin Institute for the Foundations of Learning and Data in January 2020, BIFOLD researchers achieved a wide array of advancements in the domains of Machine Learning and Big Data Management as well as in a variety of application areas by developing new Systems and creating impactfull publications. The following summary provides an overview of recent research activities and successes.